和積演算法人類基因體計畫的完成引發了一波直接透過核酸序列及其衍生出的氨基酸序列去解決不同的生物難題的研究。因此，可以只從任何一條蛋白質鏈的氨基酸序列去預測其三級結構的迫切需要驅使了我們開發出一種以模型為基礎的方法去預測三級結構的基本成份，也就是二級結構。為了達成這個目標，首先我們將所有符合條件的二級結構序列表達成在二級結構格狀圖裡的特定路徑，接著使用關聯圖及其貝氏網路展開的方法將一級結構與二級結構的關係予以量化。最後，藉由採取類似於編碼理論的程序，我們透過兩種解碼演算法，分別是Viterbi演算法及和積演算法，去指派每一個氨基酸的二級結構。模擬結果顯示我們提出的方法所達到的準確率與現今其他只透過序列的預測方法不相上下，而且當所要預測結構的序列限制在特定的蛋白質折疊時可以得到更佳的結果。

The completion of Human Genome Project has triggered a wave of investigating various
biological problems directly through the string of nucleotides and also its derived amino
acid sequence. Therefore, the urgent need of predicting protein three-dimensional structure
simply from the amino acid sequence propels us to develop a model-based method to predict
the composition of the fundamental structural elements–that is, secondary structures–of any
protein chain. To accomplish this goal, we first represent all the eligible secondary structure
sequences as specific paths in a secondary structure trellis. Then we employ the method
of dependency graphs and their expanded Bayesian networks to quantify the relationship
between primary and secondary structures. Following the similar procedure as in the coding
theory, we finally assign a secondary structure element to each amino acid through the use of
two decoding algorithms: the Viterbi algorithm and the sum-product algorithm. The simulation
results reveal that our proposed method achieves an accuracy that is indistinguishable
from other existing sequence-only methods, and that a better outcome is reached when the
target sequences are confined to a specific protein fold.

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